The History of AI: Presented by GPT 4o + Gamma
Today I’m testing GAMMA an AI-Powered slide creator. My goal, make a quick and useful AI timeline. I'll use ChatGPT 4o to help me with the content and Gamma to do all the formatting and layout. Let’s see how long it takes and what the end results looks like.
Explore Timeline
1970s-1980s: Early Business Applications
1
MYCIN Expert System
In the 1970s, MYCIN helped doctors diagnose infections and suggest treatments, revolutionizing healthcare with AI.
2
AI in Finance
AI began aiding banks in risk assessment and fraud detection, improving financial decision-making.
3
Shakey the Robot
Shakey, developed in the early 1970s, was the first robot to integrate perception, reasoning, and action, paving the way for future robotics.
1990s: AI Meets the Internet
Online Customer Service Bots
The internet boom in the 1990s led to the development of online customer service bots, setting the stage for future advancements in conversational AI.
Deep Blue
In 1997, IBM's Deep Blue made history by defeating world chess champion Garry Kasparov, showcasing the potential of AI in strategic thinking and problem-solving.
Speech Recognition Advances
Dragon NaturallySpeaking, released in 1997, brought significant improvements in speech recognition technology, making it more accessible for everyday use.
Early 2000s: The Rise of Machine Learning
1
Machine Learning Algorithms
The early 2000s saw a surge in the development and application of machine learning algorithms, enabling computers to learn from data and improve over time.
2
Spam Filters
Machine learning began to be widely used in email spam filters, dramatically improving the ability to detect and block unwanted emails.
3
Personalized Recommendations
Companies like Amazon and Netflix started using machine learning for personalized recommendations, enhancing user experience by suggesting products and content based on individual preferences.
4
Self-Driving Cars
Early prototypes of self-driving cars emerged, utilizing machine learning for object detection, path planning, and decision-making on the road.
2010s: Big Data and Advanced Machine Learning
Big Data
The explosion of big data in the 2010s allowed AI systems to process and analyze vast amounts of information, leading to more accurate and sophisticated models.
Deep Learning
Deep learning, a subset of machine learning, gained prominence, enabling breakthroughs in image and speech recognition.
AI in Healthcare
AI began to significantly impact healthcare, aiding in diagnostics, personalized medicine, and the development of new drugs.
Virtual Assistants
Virtual assistants like Siri, Alexa, and Google Assistant became widespread, utilizing AI to understand and respond to user queries.
Late 2010s: Strategic AI Integration
AI in Business
Companies across industries integrated AI into their core strategies, optimizing operations, improving customer service, and driving innovation.
Autonomous Vehicles
The development of autonomous vehicles accelerated, with companies like Tesla and Waymo making significant advancements.
AI in Cybersecurity
AI became crucial in cybersecurity, helping to detect and respond to threats in real time.
Facial Recognition
Facial recognition technology saw widespread adoption, though it also raised concerns about privacy and ethical implications.
AI's Rapid Evolution: 2020-Present
1
2020
  • June 2020: OpenAI releases GPT-3, a powerful language model capable of generating human-like text, marking a significant leap in natural language processing.
  • October 2020: Tesla releases its Full Self-Driving (FSD) beta to select customers, a major step forward in autonomous vehicle technology.
  • October 2020: Waymo launches its fully autonomous ride-hailing service to the public in Phoenix, Arizona, demonstrating the commercial viability of self-driving cars.
  • December 2020: DeepMind's AlphaFold 2 solves the protein folding problem, a breakthrough in biology with significant implications for drug discovery and disease understanding.
2
2021
  • January 2021: OpenAI introduces DALL-E, an AI model that generates images from textual descriptions, showcasing AI's potential in creative fields like art and design.
  • April 2021: Google announces LaMDA (Language Model for Dialogue Applications), designed to improve conversational AI by enabling more natural and open-ended conversations.
  • May 2021: IBM unveils Project CodeNet, a dataset designed to train AI to understand and write code, pushing forward the field of AI programming.
  • June 2021: NVIDIA introduces NeRF (Neural Radiance Fields), a new AI technique for 3D scene rendering from 2D images, advancing computer graphics and virtual reality.
3
2022
  • January 2022: Meta AI releases NLLB (No Language Left Behind), an AI model aimed at providing high-quality translations for low-resource languages, promoting inclusivity in AI language models.
  • March 2022: OpenAI debuts CLIP - a precursor to DALL-E. A model that understands images and their textual descriptions, enhancing AI's ability to perform visual tasks.
  • July 2022: Google's DeepMind introduces AlphaCode, an AI capable of writing code and solving programming challenges, demonstrating significant progress in AI-assisted coding.
  • November 2022: Stability AI releases Stable Diffusion, an open-source AI model for generating high-quality images from text prompts, making powerful image generation accessible to more people.
4
2023
  • March 2023: Microsoft integrates GPT-4 into its Office suite as Copilot, enhancing productivity tools with advanced AI capabilities for text generation, summarization, and insights.
  • April 2023: Anthropic launches Claude, an AI assistant designed for safe and useful conversational experiences, contributing to the diversity of AI assistants available.
  • June 2023: Google's Gemini AI integrates multi-modal capabilities, combining text, images, and other data types to provide more comprehensive and intuitive AI interactions.
  • October 2023: OpenAI releases ChatGPT-4.5, an advanced conversational AI model that improves upon previous versions with enhanced understanding and generation capabilities.
5
2024
  • February 2024: OpenAI releases DALL-E 3, featuring significant improvements in image generation quality and versatility, further pushing the boundaries of creative AI.
  • April 2024: Microsoft launches the Azure AI Supercomputer, offering unprecedented computational power for training and deploying large-scale AI models.
  • May 2024: Meta AI releases AI-powered AR glasses, combining augmented reality with advanced AI features for real-time language translation, object recognition, and more.
So what do we think?
I time-boxed this exercise to 1.5 hours. That included account creation, very inefficient copy and past technique, minimal content editing, lots of prompt adjusting with GPT4.0 and some work on the slide design. I chose a pre-set theme for the design. Note, the themes leave much to be desired. I could've set up my own styling, but that wasn't the point.

I was able to edit content within the template layout just like a I would edit most slides. The UI is intuitive and easy to follow, so that's good news. But when I asked AI to do things for me in terms of layout and structure, it failed every time. It seems the AI is mostly there to parse content and to generate content. However, don't look for very sophisticated or reliable generated content from Gamma. I asked it to write a paragraph about itself and it proudly announced that Gamma is Anthropic's flagship product. Ha.

Also the context window is limited - figure out the size of the context window.
Figure out what model is being used.
Introducing Gamma - The Advanced AI Platform
Gamma is Anthropic's flagship AI product, built upon our cutting-edge foundation model. Leveraging a sophisticated context window and advanced training techniques, Gamma offers unparalleled capabilities in natural language processing, task completion, and multi-modal intelligence. With flexible pricing options and enterprise-grade security, Gamma is the premier choice for businesses seeking to harness the power of transformative AI.
Foundation Model: Gamma is powered by Anthropic's proprietary foundation model, trained on a vast corpus of data to achieve broad and robust understanding. This foundation model serves as the core intelligence of the Gamma platform, enabling rapid adaptation and customization for diverse use cases.
Context Window: Gamma's expansive context window allows it to maintain coherent, long-form conversations and tackle complex, multi-step tasks with ease. By retaining and reasoning over extensive contextual information, Gamma delivers truly intelligent and contextual responses.
Pricing: Gamma offers flexible pricing structures to meet the needs of businesses of all sizes. From pay-as-you-go models to custom enterprise solutions, Gamma's pricing is designed to be transparent, scalable, and aligned with your organization's AI adoption journey.
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